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Journal of Peking University (Health Sciences) ›› 2025, Vol. 57 ›› Issue (6): 1042-1050. doi: 10.19723/j.issn.1671-167X.2025.06.005

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Untargeted metabolomics reveals differential serum metabolites and metabolic pathways in patients with primary Sjögren syndrome and thrombocytopenia

Zhao XIANG1, Li YANG2, Jing YANG2,*()   

  1. 1. Department of Rheumatology and Immunology, Dazhou Central Hospital, Dazhou 625000, Sichuan, China
    2. Department of Rheumatology and Immunology, Mianyang Central Hospital, Mianyang 621000, Sichuan, China
  • Received:2025-08-13 Online:2025-12-18 Published:2025-11-25
  • Contact: Jing YANG
  • Supported by:
    Key Research and Development Project of Science and Technology Department of Sichuan Provincial(2017SZ0148)

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Abstract:

Objective: To systematically compare serum metabolome differences between patients with thrombocytopenia in primary Sjögren syndrome (pSS) and those with normal platelet count using non- targeted metabolomics technology, so as to identify differential metabolites, analyze the relationship between the relative quantification of these metabolites and platelet counts, and screen metabolic pathways associated with platelet counts in pSS patients with thrombocytopenia. Methods: The patients with pSS were selected and grouped according to the presence or absence of thrombocytopenia. Serum samples were collected from the study subjects and analyzed by liquid chromatography-mass spectrometry (LC-MS). The samples were analysed by human metabolome database (HMDB), lipid metabolites and pathways strategy (LIPID MAPS) and other databases for classification and annotation. The samples were analyzed by principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) for multi-variate statistical analysis to screen the differential metabolites between the groups, and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis was made to study the functions and metabolic pathways of the metabolites. Correlation analysis was performed between the abundance of serum differential metabolites and platelet counts of pSS patients with thrombocytopenia. Results: This study included 62 patients with pSS, of whom 32 had thrombocytopenia and 30 had normal platelet counts. A total of 137 differentially expressed metabolites, enriched in 54 metabolic pathways, were found in the serum of patients with thrombocytopenia compared with those without thrombocytopenia. Among them, the expression of desoxycorticosterone, hydrocortisone, and taurine was positively correlated with platelet count, and the expression of neopterin was negatively correlated with platelet count. Enrichment analysis showed that desoxycorticosterone and hydrocortisone were enriched in the steroid hormone biosynthesis pathway, taurine was enriched in the metabolic pathway of taurine and taurine, and neopterin was enriched in the folate metabolic pathway. Conclusion: Thrombocytopenia in pSS patients may be related to the reduced activity of steroid hormone biosynthesis pathway and the metabolic pathway of taurine and taurine, and the increased activity of the pathway of folate metabolism.

Key words: Sjögren syndrome, Thrombocytopenia, Metabolomics, Serum, Biomarkers

CLC Number: 

  • R593.2

Table 1

Clinical and demographic characteristics of the study population"

Clinical and demographic characteristics pSS with thrombocytopenia (n=32) pSS without thrombocytopenia (n=30) P
Age/years, $\bar x \pm s$ 52.84±13.33 47.9±11.96 0.140 0
Female, n(%) 31 (96.88) 29 (93.33) 0.999 9
Course of disease/years, $\bar x \pm s$ 5.73±5.15 1.65±2.77 0.000 2
ESSDAI, $\bar x \pm s$ 2.59±0.49 1.13±0.67 ＜0.000 1
Platelet count/(×109/L), $\bar x \pm s$ 39.72±28.31 181±55.23 ＜0.000 1

Figure 1

PCA score (A, B) and PLS-DA score (C, D) A and B show the PCA score plots of metabolites in positive and negative ion modes; C and D show the PLS-DA score plots of metabolites in positive and negative ion modes. pSS_lowPLT, primary Sjögren syndrome (pSS) with thrombocytopenia group; pSS_normalPLT, pSS without thrombocytopenia group; PC, principal component; PCA, principal component analysis; PLS-DA, partial least squares discriminant analysis."

Figure 2

Volcano plot of differential metabolites A and B show the volcano plots of positive and negative ion patterns in the primary Sjögren syndrome (pSS) with thrombocytopenia and pSS without thrombocytopenia groups. Points in the volcano plot indicate individual metabolites, with red dots representing significantly upregulated metabolites and green dots indicating significantly downregulated ones. The grey dots represent metabolites with no significant difference. The variable importance in the projection (VIP) value is indicated by the size of the points."

Figure 3

KEGG enrichment pathway bubble plot A and B display the pathway enrichment results of differential metabolites between the primary Sjögren syndrome (pSS) with thrombocytopenia and pSS without thrombocytopenia groups in positive and negative ion modes. Point colors indicate P-values from hypergeometric tests, where smaller values signify more statistically significant results. Point sizes reflect the number of enriched differential metabolites within each pathway, with larger points indicating higher abundance. KEGG, Kyoto Encyclopedia of Genes and Genomes; TCA, tricarboxylic acid."

Figure 4

Relationship between differential metabolites abundance and platelet count A, normal Q-Q plots of 34 differentially metabolized compounds; B, normal Q-Q plot of platelets, where the x-axis represents actual observed values and the y-axis indicates predicted values. C, Pearson linear correlation plot between 2 differentially metabolized compounds and platelets; D, Spearman linear correlation plot between 9 metabolites and platelets. LPE, lysophosphatidylethanolamine; GHB, γ-hydroxybutyric acid; AIMP, aminoacyl-tRNA synthetase-interacting multifunctional protein; PLT, platelet."

Table 2

Relationship between expression of 11 differential metabolites and platelet count"

Differential metabolite r P q Up/Down KEGG pathway
Taurine 0.376 7 0.036 7 0.041 9 Down Aurine and hypotaurine metabolism
Neopterin -0.578 1 0.000 7 0.007 7 Down Folate biosynthesis
Imidazolelactic acid -0.391 5 0.026 7 0.041 9 Up
P-toluenesulfonic acid -0.404 1 0.021 8 0.041 9 Down
Tetradecanedioic acid 0.365 4 0.039 7 0.041 9 Up
Guggulsterone 0.361 8 0.041 9 0.041 9 Down
Phosphatidylcholine (7 ∶0/8 ∶0) 0.391 5 0.026 7 0.041 9 Down
Desoxycortone 0.425 0 0.015 3 0.041 9 Down Steroid hormone biosynthesis
Cortisol 0.380 6 0.031 6 0.041 9 Down Steroid hormone biosynthesis
10-nitrolinoleate 0.535 8 0.001 6 0.008 8 Up
(11E, 15Z)-9, 10, 13-trihydroxyoctadeca-11, 15-dienoic acid 0.403 7 0.021 9 0.041 9 Up
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